AI training data, annotation and LLM evaluation services for AI builders
Innodata is a public data engineering company producing training, annotation and evaluation data for generative and traditional AI. Its stack pairs open-weight model tooling (Llama, Mistral, Qwen, vLLM, llama.cpp) with vector stores like FAISS and Milvus, and it is now adopting OpenAI and Anthropic. Hiring is accelerating and overwhelmingly data-focused, spread across the US, Philippines, India, Europe and francophone Africa, which points to a scaled, multilingual annotation workforce.
Notable leadership hires: Lead Generation Specialist, Quality Lead
Innodata Inc. is a NASDAQ-listed data engineering company headquartered in Ridgefield Park, New Jersey, founded in 1988. It supplies data, platforms and services to technology companies and enterprises that build or adopt AI, covering data collection, annotation, extraction, cleansing and model evaluation. The company employs between 5,001 and 10,000 people. Its delivery footprint spans the United States, the Philippines, India, France, Germany, Bulgaria and several North and West African countries.
Innodata uses Python, C++, Java and Rust with PyTorch, RAG, CVAT, Databricks, open-weight models like Llama, Mistral and Qwen, vLLM, FAISS, Milvus, BigQuery, AWS and Salesforce, and is adopting OpenAI and Anthropic.
Innodata hires in the United States, Philippines, India, France, Germany, Bulgaria, Côte d'Ivoire, Gabon, Morocco, Tunisia and Algeria, with hiring concentrated in data roles.
Current work includes QSA dataset development, LLM training data generation, prompt creation, LLM evaluation, synthetic data integration, AI annotation and translation, and testing a RAG-based medical literature assistant.
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Innodata Inc.'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.